# Path Configuration from tools.preprocess import * # Processing context trait = "Essential_Thrombocythemia" cohort = "GSE55976" # Input paths in_trait_dir = "../DATA/GEO/Essential_Thrombocythemia" in_cohort_dir = "../DATA/GEO/Essential_Thrombocythemia/GSE55976" # Output paths out_data_file = "./output/preprocess/3/Essential_Thrombocythemia/GSE55976.csv" out_gene_data_file = "./output/preprocess/3/Essential_Thrombocythemia/gene_data/GSE55976.csv" out_clinical_data_file = "./output/preprocess/3/Essential_Thrombocythemia/clinical_data/GSE55976.csv" json_path = "./output/preprocess/3/Essential_Thrombocythemia/cohort_info.json" # Get relevant file paths soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir) # Extract background info and clinical data from the matrix file background_info, clinical_data = get_background_and_clinical_data(matrix_file_path) # Get dictionary of unique values per row in clinical data unique_values_dict = get_unique_values_by_row(clinical_data) # Print background info print("Background Information:") print("-" * 50) print(background_info) print("\n") # Print clinical data unique values print("Sample Characteristics:") print("-" * 50) for row, values in unique_values_dict.items(): print(f"{row}:") print(f" {values}") print() # 1. Gene Expression Data Availability # The title and study design indicate gene expression profiling using cDNA microarrays is_gene_available = True # 2. Variable Availability and Data Type Conversion # 2.1 Row identifiers in sample characteristics trait_row = 0 # Disease condition is in row 0 age_row = None # Age not available gender_row = None # Gender not available # 2.2 Conversion functions def convert_trait(value): if not isinstance(value, str): return None value = value.split(': ')[-1].strip().lower() # Convert to binary for Essential Thrombocythemia if 'essential thrombocythemia' in value: return 1 elif value in ['polycythemia vera (pv)', 'primary myelofibrosis jak2+', 'primary myelofibrosis jak2-', 'chronic myelogenous leukemia', 'healthy donor']: return 0 return None def convert_age(value): # Not used since age data unavailable return None def convert_gender(value): # Not used since gender data unavailable return None # 3. Save initial filtering metadata validate_and_save_cohort_info( is_final=False, cohort=cohort, info_path=json_path, is_gene_available=is_gene_available, is_trait_available=(trait_row is not None) ) # 4. Extract clinical features if trait_row is not None: selected_clinical = geo_select_clinical_features( clinical_df=clinical_data, trait=trait, trait_row=trait_row, convert_trait=convert_trait, age_row=age_row, convert_age=convert_age, gender_row=gender_row, convert_gender=convert_gender ) # Preview the extracted features preview = preview_df(selected_clinical) print("Preview of selected clinical features:") print(preview) # Save to CSV selected_clinical.to_csv(out_clinical_data_file) # Extract gene expression data genetic_data = get_genetic_data(matrix_file_path) # Print first 20 probe IDs print("First 20 probe IDs:") print(genetic_data.index[:20]) # These identifiers (6590728, 6590730, etc.) appear to be probe IDs, not human gene symbols # They look like Illumina probe IDs which need to be mapped to gene symbols requires_gene_mapping = True # Extract gene annotation from SOFT file gene_annotation = get_gene_annotation(soft_file_path) # Preview column names and first few values preview_dict = preview_df(gene_annotation) print("Column names and preview values:") for col, values in preview_dict.items(): print(f"\n{col}:") print(values) # 1. Identify columns for gene identifier and gene symbol # In gene_annotation, 'ID' column matches probe IDs in genetic_data # 'GENE SYMBOL' column contains the corresponding gene symbols prob_col = 'ID' gene_col = 'GENE SYMBOL' # 2. Get gene mapping dataframe mapping_data = get_gene_mapping(gene_annotation, prob_col, gene_col) # 3. Convert probe-level data to gene expression data gene_data = apply_gene_mapping(genetic_data, mapping_data) # 1. Normalize gene symbols and save normalized gene data normalized_gene_data = normalize_gene_symbols_in_index(gene_data) normalized_gene_data.to_csv(out_gene_data_file) # Read the processed clinical data file clinical_df = pd.read_csv(out_clinical_data_file, index_col=0) # Link clinical and genetic data using the normalized gene data linked_data = geo_link_clinical_genetic_data(clinical_df, normalized_gene_data) # Handle missing values systematically linked_data = handle_missing_values(linked_data, trait) # Detect bias in trait and demographic features, remove biased demographic features is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait) # Validate data quality and save cohort info note = "Expression data comparing patients with Essential Thrombocythemia to controls with other myeloproliferative disorders (PMF, PV). No age or gender data available." is_usable = validate_and_save_cohort_info( is_final=True, cohort=cohort, info_path=json_path, is_gene_available=True, is_trait_available=True, is_biased=is_biased, df=linked_data, note=note ) # Save linked data if usable if is_usable: linked_data.to_csv(out_data_file) else: print(f"Dataset {cohort} did not pass quality validation and will not be saved.")